PAPER PRESENTATION
ON
DNA COMPUTING
AUTHORS
D.v.v.Satyanarayana, Ch.Ravi Kiran,
B.tech 3rd year cse, B.tech 3rd year cse,
Pragati engineering college, Pragati engineering college,
Surampalem, Surampalem.
A.P. A.P.
ADDRESS
D.V.V.SATYANARAYANA, CH.RAVI KIRAN,
S/O D.V.RAMA RAO, S/O CH.NARAYANA,
B.PALEM, SUBBARAOSTREET,
KIRLAMPIDI MANDALEM, GANDINAGAR,
E.G.DT D/O: 8-24-34, KAKINADA
PIN NO -533 437 PIN NO -533 004
EMAIL:[email protected] EMAIL:[email protected]
PHONE NO : 9948445513
Abstract.
DNA computing is a discipline that aims at harnessing individual molecules at the
nanoscopic level for computational purposes. Computation with DNA molecules
possesses an inherent interest for researchers in computers and biology. Given its vast
parallelism and high-density storage, DNA computing approaches are employed to
solve many combinatorial problems. However, the exponential scaling of the solution
space prevents applying an exhaustive search method to problem instances of realistic
size, and therefore artificial intelligence models are used in designing methods that are
more efficient.
"DNA has also been explored as an excellent material and a fundamental building
block for building large-scale nanostructures, constructing individual
nanomechanical devices, and performing computations."
History
Working in the 19th century,
biochemists initially isolated DNA and
RNA (mixed together) from cell nuclei.
They were relatively quick to appreciate
the polymeric nature of their "nucleic
acid" isolates, but realized only later that
nucleotides were of two types--one
containing ribose and the other
deoxyribose. It was this subsequent
discovery that led to the identification
and naming of DNA as a substance
distinct from RNA.
In 1994, Leonard M. Ad leman solved
an unremarkable computational problem
with a remarkable technique. It was a
problem that a person could solve it in a
few moments or an average desktop
machine could solve in the blink of an
eye. It took Ad leman, however, seven
days to find a solution. Why then was
this work exceptional? Because he
solved the problem with DNA. "DNA
was a landmark demonstration of
computing on the molecular level."
Ad leman’s demonstration only involves
seven cities, making it in some sense a
trivial problem that can easily be solved
by inspection. Nevertheless, his work is
significant for a number of reasons.
It illustrates the possibilities of using
DNA to solve a class of problems that is
difficult or impossible to solve using
traditional computing methods. It's an
example of computation at a molecular
level, potentially a size limit that may
never be reached by the semiconductor
industry. It demonstrates unique aspects
of DNA as a data structure. It
demonstrates that computing with DNA
can work in a massively parallel fashion.
Overview of DNA computing
The general structure of a section of
DNA Deoxyribonucleic acid (DNA) is
a nucleic acid — usually in the form of
a double helix that contains the genetic
instructions or genocode monitoring the
biological development of all cellular
forms of life, and many viruses. DNA is
a long polymer of nucleotides (a polyn
ucleotide) and encodes the sequence of
the amino acid residues in proteins using
the genetic code, a triplet code of
nucleotides.
The fact that one of the most
fundamental building blocks of life,
deoxyribonucleic acid, DNA for short,
can be used to compute solutions to
combinatorial problems has been
demonstrated by Adleman in 1994.
Using double stranded DNA he solved
an 7 node instance for the Hamiltonian
path problem. The field of DNA
computing has evolved rapidly since
1994, algorithms for different
combinatorial problems have been
proposed, different models of
computation with DNA have been
considered and many experiments have
been conducted. DNA computing aims
at using nucleic acids for computing.
Since micro molar DNA solutions can
act as billions of parallel nanoprocessors,
DNA computers can in theory solve
optimization problems that require vast
search spaces
What is DNA computing?
DNA computing is the ability to drive
computations, store data, and retrieve
data through the structure and use of
DNA molecules. To understand DNA
computing, we must first understand the
molecule. Molecules are made up of
atoms; the atom contains protons,
neutrons, and electrons. The molecule
can be a combination of atoms (such as a
water molecule, H2O). DNA computing
takes strands of DNA with proteins,
enzymes and program specific states in
each molecule in the double-helix strand.
The idea of DNA computing is similar to
the action of DNA to begin with. One
strand of DNA houses the “RAM” or
memory, the other strand is a backup
(like Raid 0+1 on disk). The enzymes
are the motors that copy, search, access,
read/write, the information into the DNA
strands. When the DNA is put in to an
aqueous solution (like water), and the
data is added, the data or information
finds the appropriate DNA component to
combine with and attaches itself. The
data is usually in the form of a chemical
solution with its own enzymes,
providing motion or movement to the
atoms. Once the atoms bind, they
cannot be unbound without changing the
environment. Changing the environment
may mean making it “unfriendly” to the
data, thus the enzymes uncouple the
chemically bonded elements (data) and
return it to its previous state.
DNA computing, also known as
molecular computing, is a new approach
to massively parallel computation based
on groundbreaking work by Adleman.
He used DNA to solve a seven-node
Hamiltonian path problem, a special case
of an NP-complete problem that
attempts to visit every node in a graph
exactly once.
A DNA computer is basically a
collection of specially selected DNA
strands whose combinations will result
in the solution to some problem.
Technology is currently available both to
select the initial strands and to filter the
final solution. The promise of DNA
computing is massive parallelism: with a
given setup and enough DNA, one can
potentially solve huge problems by
parallel search. This can be much faster
than a conventional computer, for which
massive parallelism would require large
amounts of hardware, not simply more
DNA.
DNA computing and how it works
" DNA computing is a relatively new
area of computer science that
performs computation in a very
unique way: rather than using electric
circuits and logic gates, it uses DNA
molecules and chemical processes."
The general principle behind typical
DNA computing is to use the intrinsic
parallelism present in chemical
reactions. The basic approach is to start
with a mixture of DNA strands that
generate a so-called library of all
potential solutions to the problem. Then,
various operations are performed which
eventually select the correct solution
from the library. Typical operations
which will be discussed and whose use
will be illustrated include the "begins-
with" selector, the "ends-with" selector,
the "contains substring" selector, and
"sort-by-length" (gel electrophoresis).
All of them use common biochemical
techniques.
How do we model systems like this?
In 1994 a DNA computing experiment
proved that data can be stored,
replicated, searched and retrieved from
DNA structures. “DNA bases
represented the information bits: ATCG
(nucleotides) spaced every 0.35
nanometers along the DNA molecule,
giving DNA a remarkable data density
of nearly 18 Mbits per inch.” This
provides hope for computing power.
Each nucleotide can represent a bit.
Not only does the bit type make a
difference, but the order or sequence as
well. A “T” in a third position means
something completely different than a
“T” in the first position, leading to
limitless possibilities for computation.
Furthermore, each of these nucleotides
can be complemented by S’ and
hybridized. In other words, they can
produce double stranded DNA. For
error correction this is very important. It
gives the nano-computer a chance to
correct what should be a comparable
equivalent (copy) of the data. Such is
the way of Raid 0+1 disk arrays.
The Nanohousing software must allow
for intermixture of chemical models,
bonding and surface areas. Moving
forward, we will have to think and
construct systems and interactions in
multi-dimensional space and in parallel.
Thinking and coding in parallel won’t
work anymore.
Molecular structure
Comparisons between DNA and single
stranded RNA with the diagram of the
bases showing.Although sometimes
called "the molecule of heredity", DNA
macromolecules as people typically
think of them are not single molecules.
Rather, they are pairs of molecules,
which entwine like vines to form a
double helix (see the illustration at the
right).
Each vine-like molecule is a strand of
DNA: a chemically linked chain of
nucleotides, each of which consists of a
sugar (deoxyribose), a phosphate and
one of five kinds of nucleobases
("bases"). Because DNA strands are
composed of these nucleotide subunits,
they are polymers.
There are five kinds of nucleotides,
which are commonly referred to by
the identity of their bases. These are
adenine (A), thymine (T), uracil (U),
cytosine (C), and guanine (G). U is
rarely found in DNA except as a result
of chemical degradation of C, but in
some viruses, notably PBS1 phage
DNA, U completely replaces the usual T
in its DNA. Similarly, RNA usually
contains U in place of T, but in certain
RNAs such as transfer RNA, T is always
found in some positions. Thus, the only
true difference between DNA and RNA
is the sugar, 2-deoxyribose in DNA and
ribose in RNA.
In a DNA double helix, two
polynucleotide strands can associate
through the hydrophobic effect and pi
stacking. Specificity of which strands
stay associated is determined by
complementary pairing. Each base forms
hydrogen bonds readily to only one
other, A to T forming two hydrogen
bonds and C to G forming three
hydrogen bonds. The GC content and
length of each DNA molcule dictates the
strength of the association; the more
complementary bases exist, the stronger
and longer-lasting the association
characterised by the temperature
required to break the hydrogen bonding,
its Tm value.
The chemical structure of DNA The
cell's machinery is capable of melting or
disassociating a DNA double helix, and
using each DNA strand as a
Template for synthesizing a new strand
which is nearly identical to the previous
strand. Errors that occur in the synthesis
are known as mutations. The process
known as PCR (polymerase chain
reaction) mimics this process in vitro in
a nonliving system.
Because pairing causes the nucleotide
bases to face the helical axis, the sugar
and phosphate groups of the nucleotides
run along the outside; the two chains
they form are sometimes called the
"backbones" of the helix. In fact, it is
chemical bonds between the phosphates
and the sugars that link one nucleotide to
the next in the DNA strand.
Applications of DNA computing
The unique properties of DNA make it
a fundamental building block in the
fields of super molecular chemistry,
nanotechnology, nano-circuits,
molecular switches, molecular devices,
and molecular computing. In our
recently introduced autonomous
molecular automaton, DNA molecules
serve as input, output, and software, and
the hardware consists of DNA restriction
and ligation enzymes using ATP as fuel.
In addition to information, DNA stores
energy, available on hybridization of
complementary strands or hydrolysis of
its phosphodiester backbone. Here we
show that a single DNA molecule can
provide both the input data and all of the
necessary fuel for a molecular
automaton. Each computational step of
the automaton consists of a reversible
software molecule/input molecule
hybridization followed by an irreversible
software-directed cleavage of the input
molecule, which drives the computation
forward by increasing entropy and
releasing heat. The cleavage uses a
hitherto unknown capability of the
restriction enzyme Foci, which serves as
the hardware, to operate on a
noncovalent software/input hybrid. In
the previous automaton, software/input
ligation consumed one software
molecule and two ATP molecules per
step. As ligation is not performed in this
automaton, a fixed amount of software
and hardware molecules can, in
principle, process any input molecule of
any length without external energy
supply.
In terms of speed and size, however,
DNA computers surpass conventional
computers.
While scientists say silicon chips cannot
be scaled down much further, the DNA
molecule found in the nucleus of all cells
can hold more information in a cubic
centimeter than a trillion music CDs. A
spoonful of Shapiro's "computer soup"
contains 15,000 trillion computers. And
its energy-efficiency is more than a
million times that of a PC. While a
desktop PC is designed to perform one
calculation very fast, DNA strands
produce billions of potential answers
simultaneously. This makes the DNA
computer suitable for solving "fuzzy
logic" problems that have many possible
solutions rather than the either/or logic
of binary computers. In the future, some
speculate, there may be hybrid machines
that use traditional silicon for normal
processing tasks but have DNA co-
processors that can take over specific
tasks they would be more suitable for.
Doctors in a cell:
Perhaps most importantly, DNA
computing devices could revolutionize
the pharmaceutical and biomedical
fields. Some scientists predict a future
where our bodies are patrolled by tiny
DNA computers that monitor our well-
being and release the right drugs to
repair damaged or unhealthy tissue.
"Autonomous bio-molecular computers
may be able to work as 'doctors in a cell,'
operating inside living cells and sensing
anomalies in the host," said Shapiro.
"Consulting their programmed medical
knowledge, the computers could respond
to anomalies by synthesizing and
releasing drugs."
Molecular Computing:
A ground-breaking paper by Leonard
Adleman in 1994 presented a method for
solving the Hamilton Path Problem
using liquid-phase DNA chemistry and
demonstrated that the algorithm can be
executed in a laboratory. Advantage of
using biological molecules such as DNA
for computation lies in the fact that they
have storage capacity and that the
operations can be conducted at room
temperature. In search of killer
applications of molecular computation,
researchers have been exploring various
molecular-based computational models
and algorithms.
DNA Processing in Ciliates :
DNA Computing is one of the new
exciting developments in computer
science. One branch of this area, DNA
Computing in vivo, studies
computational processes in living cells.
In our lecture we will discuss the
computational aspects of DNA
processing in ciliates. Ciliates, a very
ancient group of organisms, have
evolved extraordinary ways of
organizing, manipulating, and
replicating the DNA in their
micronuclear genomes. Especially
interesting from the computational point
of view is the process of gene assembly,
Conclusion
On the side of the "hardware”
improvements in biotechnology are
happening at a rate similar to the
advances made in the semiconductor
industry. For instance, look at
sequencing; what once took a graduate
student 5 years to do for a PhD thesis
takes Celera just one day. With the
amount of government funded research
dollars flowing into genetic-related R&D
and with the large potential payoffs from
the lucrative pharmaceutical and
medical-related markets, this isn't
surprising. Just look at the number of
advances in DNA-related technology
that happened in the last five years.
Today we have not one but several
companies making "DNA chips," where
DNA strands are attached to a silicon
substrate in large arrays (for example
Affymetrix's gene chip). Production
technology of MEMS is advancing
rapidly, allowing for novel integrated
small scale DNA processing devices.
The Human Genome Project is
producing rapid innovations in
sequencing technology.
"The future of DNA manipulation Is
speed,automation and miniaturizatio"
And of course we are talking about DNA
here, the genetic code of life itself. It
certainly has been the molecule of this
century and most likely the next one.
"Considering all the attention that
DNA has garnered, it isn’t too hard to
imagine that one day we might have
the tools and talent to produce a small
integrated desktop machine that uses
DNA, or a DNA-like biopolymer, as a
computing substrate along with set of
designer enzymes."
Perhaps it won’t be used to play Quake
IV or surf the web -- things that
traditional computers are good at -- but it
certainly might be used in the study of
logic, encryption, genetic programming
and algorithms, automata, language
systems, and lots of other interesting
things that haven't even been invented
yet.
References
Leonard M. Adleman (1994-11-
11). "Molecular Computation Of
Solutions To Combinatorial
Problems". Science (journal) 266
(11): 1021–1024. — The first
DNA computing paper.
Describes a solution for the
directed Hamiltonian path
problem.
Martyn Amos (June 2005).
Theoretical and Experimental
DNA Computation. Springer.
ISBN 3-540-65773-8. — The
first general text to cover the
whole field.
Dan Boneh , Christopher
Dunworth, Richard J. Lipton, and
Jiri Sgall (1996). "On the
Computational Power of DNA".
DAMATH: Discrete Applied
Mathematics and Combinatorial
Operations Research and
Computer Science 71. —
Describes a solution for the
boolean satisfiability problem.
Gheorge Paun, Grzegorz
Rozenberg, Arto Salomaa
(October 1998). DNA Computing
- New Computing Paradigms.
Springer-Verlag. ISBN 3-540-
64196-3. — The book starts
with an introduction to DNA-
related matters, the basics of
biochemistry and language and
computation theory, and
progresses to the advanced
mathematical theory of DNA
computing.
Lila Kari, Greg Gloor, Sheng Yu
(January 2000). "Using DNA to
solve the Bounded Post
Correspondence Problem".
Theoretical Computer Science
